|
import argparse |
|
import os |
|
|
|
import torch |
|
import torch.nn.functional as F |
|
import torchaudio |
|
|
|
from api import TextToSpeech, load_conditioning |
|
from utils.audio import load_audio, get_voices |
|
from utils.tokenizer import VoiceBpeTokenizer |
|
|
|
def split_and_recombine_text(texts, desired_length=200, max_len=300): |
|
|
|
texts = [s.strip() + "." for s in texts.split('.')] |
|
|
|
i = 0 |
|
while i < len(texts): |
|
ltxt = texts[i] |
|
if len(ltxt) >= desired_length or i == len(texts)-1: |
|
i += 1 |
|
continue |
|
if len(ltxt) + len(texts[i+1]) > max_len: |
|
i += 1 |
|
continue |
|
texts[i] = f'{ltxt} {texts[i+1]}' |
|
texts.pop(i+1) |
|
return texts |
|
|
|
if __name__ == '__main__': |
|
parser = argparse.ArgumentParser() |
|
parser.add_argument('--textfile', type=str, help='A file containing the text to read.', default="data/riding_hood2.txt") |
|
parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) ' |
|
'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='patrick_stewart') |
|
parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/longform/') |
|
parser.add_argument('--generation_preset', type=str, help='Preset to use for generation', default='standard') |
|
args = parser.parse_args() |
|
|
|
outpath = args.output_path |
|
voices = get_voices() |
|
selected_voices = args.voice.split(',') |
|
for selected_voice in selected_voices: |
|
voice_outpath = os.path.join(outpath, selected_voice) |
|
os.makedirs(voice_outpath, exist_ok=True) |
|
|
|
with open(args.textfile, 'r', encoding='utf-8') as f: |
|
text = ''.join([l for l in f.readlines()]) |
|
texts = split_and_recombine_text(text) |
|
tts = TextToSpeech() |
|
|
|
if '&' in selected_voice: |
|
voice_sel = selected_voice.split('&') |
|
else: |
|
voice_sel = [selected_voice] |
|
cond_paths = [] |
|
for vsel in voice_sel: |
|
if vsel not in voices.keys(): |
|
print(f'Error: voice {vsel} not available. Skipping.') |
|
continue |
|
cond_paths.extend(voices[vsel]) |
|
if not cond_paths: |
|
print('Error: no valid voices specified. Try again.') |
|
|
|
priors = [] |
|
for j, text in enumerate(texts): |
|
conds = priors.copy() |
|
for cond_path in cond_paths: |
|
c = load_audio(cond_path, 22050) |
|
conds.append(c) |
|
gen = tts.tts_with_preset(text, conds, preset=args.generation_preset) |
|
torchaudio.save(os.path.join(voice_outpath, f'{j}.wav'), gen.squeeze(0).cpu(), 24000) |
|
|
|
priors.append(torchaudio.functional.resample(gen, 24000, 22050).squeeze(0)) |
|
while len(priors) > 2: |
|
priors.pop(0) |
|
|
|
|
